Sometimes, multiple parties, each owning some data, want to collaborate with each other while preserving the privacy of their own data in the meantime. In order to run analytics on multi-party data in a privacy preserving manner, various protocols are used as building blocks in data mining. Conventionally, round-robin protocols based on randomization may be used by the multiple parties under the honest-but-curious model. However, such protocols generally are not secure and tend to leak information when more than two parties collude. Also, the multiple parties may communicate with a third-party trusted mediator for analytic data and result sharing, which makes the third-party trusted mediator a weak link in the security chain. On the other hand, peer-to-peer secure protocols often involve complex computation and back-and-forth messaging between the multiple parties. Hence, it is difficult to scale these peer-to-peer secure protocols to big data analytics due to computational complexities, especially when more than two parties participate in the communications.